Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

作者：Maria Bălăeţ, Falah Alhajraf, Tanja Zerenner, Jessica Welch, Jamil Razzaque, Christine Lo, Valentina Giunchiglia, William Trender, Annalaura Lerede, Peter J. Hellyer, Sanjay Manohar, Paresh Malhotra, Michele Hu, Adam Hampshire · 发表于：npj Digital Medicine · 年份：2024 · DOI：10.1038/s41746-024-01124-6 · 被引用次数：18 · 研究领域：Parkinson's Disease Mechanisms and Treatments、EEG and Brain-Computer Interfaces、Neurological disorders and treatments

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.